Vision-Based Underwater Docking Guidance and Positioning: Enhancing Detection with YOLO-D

Abstract

This study proposed a vision-based underwater vertical docking guidance and positioning method to address docking control challenges for human-operated vehicles (HOVs) and unmanned underwater vehicles (UUVs) under complex underwater visual conditions. A cascaded detection and positioning strategy incorporating fused active and passive markers enabled real-time detection of the relative position and pose between the UUV and docking station (DS). A novel deep learning-based network model, YOLO-D, was developed to detect docking markers in real time. YOLO-D employed the Adaptive Kernel Convolution Module (AKConv) to dynamically adjust the sample shapes and sizes and optimize the target feature detection across various scales and regions. It integrated the Context Aggregation Network (CONTAINER) to enhance small-target detection and overall image accuracy, while the bidirectional feature pyramid network (BiFPN) facilitated effective cross-scale feature fusion, improving detection precision for multi-scale and fuzzy targets. In addition, an underwater docking positioning algorithm leveraging multiple markers was implemented. Tests on an underwater docking markers dataset demonstrated that YOLO-D achieved a detection accuracy of mAP@0.5 to 94.5%, surpassing the baseline YOLOv11n with improvements of 1.5% in precision, 5% in recall, and 4.2% in mAP@0.5. Pool experiments verified the feasibility of the method, achieving a 90% success rate for single-attempt docking and recovery. The proposed approach offered an accurate and efficient solution for underwater docking guidance and target detection, which is of great significance for improving the safety of docking.